Streszczenie

Analiza dostępnych danych wymagała kilku czynności, których wykonanie miało wpływ na ostateczne wyniki. Należało wybrać odpowiedni sposób uzupełniania brakujących wartości, by mozliwe było dokonanie regresji. Ze względu na to, że część cech była mocno skorelowana z datą obserwacji po kilku próbach wybrano uzupełnianie brakujących wartości przez wartość minimalną, co pozytywnie wpłynęło na wyniki regresji. Niezbędnym było również wstępne wybranie statystyk, które należy przeanalizować, z powodu ich dużej ilości. Najpierw odrzucono te statystyki które miały więcej niż połowę brakujących wartości. Następnie wybrano te najbardziej wpływające na cenę złota. Po sprawdzeniu korelacji okazało się, że na cenę złota w dużej mierze wpływa stan gospodarki - wskaźniki takie jak CPI (Consumer Price Index), S&P composite, czy Dividend. Stan rozwoju świata - liczba ludności, zmniejszający się przyrost naturalny w wielu krajach, czy edukacja również mają duży wpływ na ceny złota. Zarówno PKB na osobę, jak i PKB kraju wiąże się ze zwiększeniem wartości złota. Pokazano również, że dla poszczególnych krajów wiele czynników rozwoju koreluje ze sobą, mając wpływ na GDP (PKB) danego kraju. Przy interpretacji danych należy pamiętać jednak, że część może być przyczyną, a część skutkiem rozwoju. Zwiększająca się ilość CO2 nie jest przyczyną lecz skutkiem zmian. To wszystko spowodowało, że mając dane na temat rozwoju państw i/lub gospodarki częściowo przewidzieć ceny złota, jednak należy pamiętać, że na rzeczywistość będą zawsze wpływać czynniki, które przewidzieć dużo trudniej np. kryzysy, pandemie, wojny.

Instalacja bibliotek

r = getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
options(repos = r)
install.packages("readxl")
install.packages("corrplot")
## package 'corrplot' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\anna\AppData\Local\Temp\RtmpuAZjw9\downloaded_packages
install.packages("plotly")
## package 'plotly' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\anna\AppData\Local\Temp\RtmpuAZjw9\downloaded_packages
install.packages("heatmaply")
## package 'heatmaply' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\anna\AppData\Local\Temp\RtmpuAZjw9\downloaded_packages
install.packages("arm")
## package 'arm' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\anna\AppData\Local\Temp\RtmpuAZjw9\downloaded_packages
library("readxl")
library(dplyr)
library(tidyr)
library(ggplot2)
library(mlbench)
library(corrplot)
library(caret)
library(plotly)
library(heatmaply)
library(arm)

Wczytanie danych z plików

Ścieżka do folderu z danymi

dataDirectoryPath <- 'C:/Users/anna/OneDrive/Nauka/II semestr II stopień/Zaawansowana Eksploracja Danych/Data pack/Data pack/'

Wczytanie pliku CurrencyExchangeRates.csv

currencyExchangeRatesPath <- paste0(dataDirectoryPath, 'CurrencyExchangeRates.csv')
currencyExchangeRates <- read.csv(currencyExchangeRatesPath)

Wczytanie pliku Gold prices.csv

goldPricesPath <- paste0(dataDirectoryPath, 'Gold prices.csv')
goldPrices <- read.csv(goldPricesPath)

Wczytanie pliku S&P Composite.csv

spCompositePath <- paste0(dataDirectoryPath, 'S&P Composite.csv')
spComposite <- read.csv(spCompositePath)

Wczytanie pliku World_Development_Indicators.xlsx

worldDevelopmentIndicatorsPath <- paste0(dataDirectoryPath, 'World_Development_Indicators.xlsx')
worldDevelopmentIndicators <- read_excel(worldDevelopmentIndicatorsPath)

Czyszczenie danych na temat rozwoju państw

cleanedWorldDevelopmentIndicators <- worldDevelopmentIndicators[c(1:42600, 43666:43878),]
cleanedWorldDevelopmentIndicators <- cleanedWorldDevelopmentIndicators %>% 
  mutate_at(vars(!c('Country Name', 'Country Code', 'Series Name', 'Series Code')), as.numeric) %>%
  pivot_longer(!c('Country Name', 'Country Code', 'Series Name', 'Series Code'), names_to = "year", values_to="value")
cleanedWorldDevelopmentIndicators$year <- substr(cleanedWorldDevelopmentIndicators$year,1,4)
cleanedWorldDevelopmentIndicatorsResult <- cleanedWorldDevelopmentIndicators %>% 
  pivot_wider(names_from = c('Series Name', 'Series Code'), values_from = 'value',  names_glue = "{`Series Name`}") %>%
  mutate_at(vars(year), as.numeric)
cleanedWorldDevelopmentIndicatorsResult[cleanedWorldDevelopmentIndicatorsResult == ".." | cleanedWorldDevelopmentIndicatorsResult == ""] <- NA
cleanedWorldDevelopmentIndicatorsResult <- cleanedWorldDevelopmentIndicatorsResult %>%
select_if(colSums(!is.na(.)) > (nrow(cleanedWorldDevelopmentIndicatorsResult)/2))

Uzupełnienie brakujących wartości

n <- ncol(cleanedWorldDevelopmentIndicatorsResult)
missingValuesByCountry <- cleanedWorldDevelopmentIndicatorsResult %>% 
  group_by(`Country Name`) %>% 
  summarise_all(~sum(is.na(.))) %>% 
  transmute(`Country Name`, sumNA = rowSums(.[-1])) %>%
  arrange(desc(sumNA))
knitr::kable(head(missingValuesByCountry, 20))
Country Name sumNA
Isle of Man 4544
Sint Maarten (Dutch part) 4487
Monaco 4432
American Samoa 4389
San Marino 4378
British Virgin Islands 4318
Turks and Caicos Islands 4207
Channel Islands 4164
Cayman Islands 4127
Kosovo 4113
Liechtenstein 4026
Guam 3826
Gibraltar 3809
Andorra 3789
Virgin Islands (U.S.) 3781
Faroe Islands 3656
South Sudan 3576
Tuvalu 3568
Curacao 3556
Greenland 3556 
worldIndicatorsComplementedDf <- cleanedWorldDevelopmentIndicatorsResult %>%
    group_by(`Country Name`) %>% 
    mutate_each(funs(replace(., which(is.na(.)), min(., na.rm=TRUE)))) %>%
    mutate_each(funs(replace(., which(is.infinite(.)), 0)))
names(worldIndicatorsComplementedDf) <- gsub(" ", "_", names(worldIndicatorsComplementedDf))
names(cleanedWorldDevelopmentIndicatorsResult) <- gsub(" ", "_", names(cleanedWorldDevelopmentIndicatorsResult))
worldIndicatorsComplementedDf <- worldIndicatorsComplementedDf  %>%
  rename(CO2_emissions_residential_buildings_and_services = `CO2_emissions_from_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion)`) %>%
  rename(CO2_emissions_from_other_sectors = `CO2_emissions_from_other_sectors,_excluding_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion)`)

knitr::kable(head(worldIndicatorsComplementedDf))
Country_Name Country_Code year Urban_population_growth_(annual_%) Urban_population_(%_of_total_population) Urban_population Transport_services_(%_of_commercial_service_exports) Transport_services_(%_of_commercial_service_imports) Trade_in_services_(%_of_GDP) Trade_(%_of_GDP) Total_natural_resources_rents_(%_of_GDP) Total_greenhouse_gas_emissions_(kt_of_CO2_equivalent) Taxes_less_subsidies_on_products_(current_US\()| Taxes_less_subsidies_on_products_(current_LCU)| Taxes_less_subsidies_on_products_(constant_LCU)| Survival_to_age_65,_female_(%_of_cohort)| Survival_to_age_65,_male_(%_of_cohort)| Services,_value_added_(%_of_GDP)| Service_imports_(BoP,_current_US\)) Service_exports_(BoP,_current_US\()| Secondary_education,_pupils| Rural_population_growth_(annual_%)| Rural_population_(%_of_total_population)| Rural_population| Renewable_energy_consumption_(%_of_total_final_energy_consumption)| Renewable_electricity_output_(%_of_total_electricity_output)| Pupil-teacher_ratio,_primary| Primary_income_payments_(BoP,_current_US\)) Primary_income_receipts_(BoP,_current_US\()| Primary_school_starting_age_(years)| Portfolio_investment,_net_(BoP,_current_US\)) Portfolio_equity,net_inflows(BoP,_current_US\()| Population,_total| Population,_male| Population,_male_(%_of_total_population)| Population,_female_(%_of_total_population)| Population,_female| Population_in_urban_agglomerations_of_more_than_1_million| Population_in_the_largest_city_(%_of_urban_population)| Population_in_largest_city| Population_growth_(annual_%)| Population_density_(people_per_sq._km_of_land_area)| Population_ages_65_and_above_(%_of_total_population)| Population_ages_15-64_(%_of_total_population)| Population_ages_0-14_(%_of_total_population)| Number_of_under-five_deaths| Nitrous_oxide_emissions_(thousand_metric_tons_of_CO2_equivalent)| Nitrous_oxide_emissions_in_energy_sector_(%_of_total)| Net_primary_income_(Net_income_from_abroad)_(current_US\)) Net_primary_income_(Net_income_from_abroad)_(current_LCU) Net_primary_income_(BoP,_current_US\()| Net_official_development_assistance_received_(current_US\)) Net_domestic_credit_(current_LCU) Natural_gas_rents_(%_of_GDP) Mortality_rate,infant(per_1,000_live_births) Methane_emissions_(kt_of_CO2_equivalent) Methane_emissions_in_energy_sector_(thousand_metric_tons_of_CO2_equivalent) Merchandise_exports_to_high-income_economies_(%_of_total_merchandise_exports) Manufacturing,value_added(%_of_GDP) Life_expectancy_at_birth,total(years) Land_area_(sq._km) Labor_force,_total Inflation,consumer_prices(annual_%) Individuals_using_the_Internet_(%_of_population) Imports_of_goods_and_services_(current_US\()| Imports_of_goods_and_services_(%_of_GDP)| Gross_savings_(%_of_GDP)| Gross_national_expenditure_(%_of_GDP)| Gross_national_expenditure_(current_US\)) Gross_savings_(current_US\()| Gross_domestic_savings_(%_of_GDP)| Gross_domestic_savings_(current_US\)) Goods_exports_(BoP,_current_US\()| Goods_imports_(BoP,_current_US\)) GDP_per_capita_(current_US\()| GDP_per_capita_growth_(annual_%)| GDP_growth_(annual_%)| GDP_(current_US\)) Fuel_exports_(%_of_merchandise_exports) Fuel_imports_(%_of_merchandise_imports) Food_exports_(%_of_merchandise_exports) Food_imports_(%_of_merchandise_imports) Exports_of_goods_and_services_(current_US\()| Exports_of_goods_and_services_(annual_%_growth)| Electricity_production_from_renewable_sources,_excluding_hydroelectric_(kWh)| Electricity_production_from_renewable_sources,_excluding_hydroelectric_(%_of_total)| Electricity_production_from_oil,_gas_and_coal_sources_(%_of_total)| Electricity_production_from_coal_sources_(%_of_total)| Electricity_production_from_hydroelectric_sources_(%_of_total)| Electricity_production_from_natural_gas_sources_(%_of_total)| Electricity_production_from_nuclear_sources_(%_of_total)| Consumer_price_index_(2010_=_100)| CO2_emissions_from_solid_fuel_consumption_(%_of_total)| CO2_emissions_from_solid_fuel_consumption_(kt)| CO2_emissions_from_transport_(%_of_total_fuel_combustion)| CO2_intensity_(kg_per_kg_of_oil_equivalent_energy_use)| CO2_emissions_residential_buildings_and_services| CO2_emissions_from_other_sectors| CO2_emissions_from_manufacturing_industries_and_construction_(%_of_total_fuel_combustion)| CO2_emissions_from_liquid_fuel_consumption_(kt)| CO2_emissions_from_liquid_fuel_consumption_(%_of_total)| CO2_emissions_from_gaseous_fuel_consumption_(kt)| CO2_emissions_from_gaseous_fuel_consumption_(%_of_total)| CO2_emissions_from_electricity_and_heat_production,_total_(%_of_total_fuel_combustion)| CO2_emissions_(metric_tons_per_capita)| CO2_emissions_(kt)| CO2_emissions_(kg_per_2010_US\)_of_GDP) Birth_rate,crude(per_1,000_people) Access_to_electricity_(%_of_population)
Afghanistan AFG 1970 5.748488 11.643 1300949 6.982691 59.19951 4.926239 21.72811 0.3028816 14306.62 60000000 2700000000 7606457000 27.72562 23.52467 36.15115 103400000 8200000 116174 2.121114 88.357 9872705 11.5591 67.7305 41.22332 6900000 19200000 7 -29571652 0 11173654 5697024 50.98622 49.01378 5476630 471891 36.27283 471891 2.536744 17.11493 2.631613 53.04314 44.32524 164463 3042.256 4.823610 35557778 1600100000 -35470046 27610001 10711736612 0.0000000 200.9 10202.00 1166.628 33.71281 3.530422 37.409 652860 3066275 -6.811161 0 208888900 11.94411 0 102.1601 1786664529 0 3.303681 57777629 252300000 623500000 156.5188 -4.168191 -1.934778 1748886596 16.94615 6.105695 36.06465 20.54160 171111104 0 0 0 0 0 0 0 0 63.52339 26.09649 436.373 0 0 0 0 0 671.061 40.13158 216.353 12.93860 0 0.1496513 1672.152 0.1371468 51.502 22.29527
Afghanistan AFG 1971 5.860102 12.021 1379464 6.982691 59.19951 4.926239 27.06314 0.3739563 14391.78 64444444 2900000000 7606457000 28.39374 24.14596 36.15115 103400000 8200000 134069 2.236404 87.979 10095986 11.5591 67.7305 39.10539 6900000 19200000 7 -29571652 0 11475450 5845351 50.93788 49.06212 5630099 495303 35.90547 495303 2.665128 17.57720 2.635235 52.69327 44.67150 165306 3006.138 4.863614 37777778 1700000000 -35470046 44439999 11670336612 0.0000000 197.1 10201.50 1178.829 42.71271 3.530422 37.930 652860 3066275 -6.811161 0 295555549 16.14080 0 105.2184 1926664351 0 0.242728 4444613 252300000 623500000 159.5675 -4.168191 -1.934778 1831108971 14.55330 5.000146 27.02128 25.43237 199999989 0 0 0 0 0 0 0 0 63.52339 18.95551 359.366 0 0 0 0 0 748.068 39.45841 440.040 23.21083 0 0.1652082 1895.839 0.1371468 51.411 22.29527
Afghanistan AFG 1972 5.899299 12.410 1463291 6.982691 59.19951 4.926239 32.86908 0.4352624 13040.85 60000000 2700000000 7606457000 29.06186 24.76724 36.15115 103400000 8200000 153060 2.271405 87.590 10327931 11.5591 67.7305 40.38314 6900000 19200000 7 -29571652 0 11791222 6000895 50.89290 49.10710 5790327 528508 36.11776 528508 2.714539 18.06087 2.627456 52.43530 44.93724 166120 2530.158 5.652229 35553333 1599900000 -35470046 55180000 13556437512 0.0041971 193.4 9170.59 1262.196 35.24140 3.530422 38.461 652860 3066275 -6.811161 0 288888878 18.10585 0 103.3426 1648888947 0 3.203335 51110980 252300000 623500000 135.3172 -4.168191 -1.934778 1595555476 13.73092 6.813822 37.23807 28.46417 235555544 0 0 0 0 0 0 0 0 63.52339 12.44019 190.684 0 0 0 0 0 627.057 40.90909 300.694 19.61722 0 0.1299955 1532.806 0.1371468 51.303 22.29527
Afghanistan AFG 1973 5.823573 12.809 1551037 6.982691 59.19951 4.926239 27.69231 0.8714832 13535.75 64444444 2900000000 7606457000 29.83817 25.49372 36.15115 103400000 8200000 165346 2.202488 87.191 10557926 11.5591 67.7305 38.32161 6900000 19200000 7 -29571652 0 12108963 6157843 50.85359 49.14641 5951120 573161 36.95341 573161 2.659057 18.54756 2.609505 52.25243 45.13807 166562 2674.404 5.255055 37775556 1699900000 -35470046 55720001 13941837512 0.0479294 189.4 9403.54 1173.216 29.77436 3.530422 39.003 652860 3066275 -6.811161 0 255555562 14.74359 0 101.7949 1764444431 0 5.512817 95555493 252300000 623500000 143.1446 -4.168191 -1.934778 1733333264 11.61954 6.565215 48.62099 23.86529 224444438 0 0 0 0 0 0 0 0 63.52339 19.01566 311.695 0 0 0 0 0 704.064 42.95302 333.697 20.35794 0 0.1353666 1639.149 0.1371468 51.184 22.29527
Afghanistan AFG 1974 5.630224 13.219 1640869 6.982691 59.19951 4.926239 28.86598 1.1527195 14945.97 120000000 5400000000 7606457000 30.61448 26.22020 36.15115 103400000 8200000 172797 2.008176 86.781 10772091 11.5591 67.7305 37.21537 6900000 19200000 7 -29571652 0 12412960 6308583 50.82255 49.17745 6104377 621656 37.88578 621656 2.479517 19.01320 2.583512 52.13682 45.27967 166690 2937.318 5.028316 46666667 2100000000 -35470046 48910000 15681237512 0.1701138 185.5 9987.93 1304.340 24.15658 3.530422 39.558 652860 3066275 -6.811161 0 320000000 14.84536 0 100.8247 2173333344 0 7.938141 171111036 252300000 623500000 173.6536 -4.168191 -1.934778 2155555498 13.97424 9.325608 43.91324 22.88171 302222222 0 0 0 0 0 0 0 0 63.52339 15.86998 304.361 0 0 0 0 0 770.070 40.15296 399.703 20.84130 0 0.1545031 1917.841 0.1371468 51.058 22.29527
Afghanistan AFG 1975 5.343228 13.641 1730929 6.982691 59.19951 4.926239 26.94836 1.6405264 14574.16 91111111 4100000000 7606457000 31.39079 26.94668 36.15115 103400000 8200000 185723 1.713261 86.359 10958235 11.5591 67.7305 37.30693 6900000 19200000 7 -29571652 0 12689164 6446273 50.80140 49.19860 6242891 674254 38.95330 674254 2.200731 19.43627 2.551445 52.09805 45.35050 166442 3153.723 4.835251 51111111 2300000000 -35470046 66980003 15551937512 0.4362939 181.5 10476.60 1298.326 29.40127 3.530422 40.128 652860 3066275 -6.811161 0 337777778 14.27230 0 101.5962 2404444524 0 8.169009 193333202 252300000 623500000 186.5108 -4.168191 -1.934778 2366666616 20.29962 7.743069 36.42858 24.42357 300000007 0 0 0 0 0 0 0 0 63.52339 18.79310 399.703 0 0 0 0 0 876.413 41.20690 476.710 22.41379 0 0.1676123 2126.860 0.1371468 50.930 22.29527

Czyszczenie danych na temat cen złota

goldPricesYearly <- goldPrices
goldPricesYearly$year <- substr(goldPrices$Date,1,4)
goldPricesYearly <- group_by(goldPricesYearly, year)
goldPricesYearly <- summarize(goldPricesYearly, 
                              gold_usd = mean(`USD..AM.`, na.rm=TRUE),
                              gold_gbp = mean(`GBP..AM.`, na.rm=TRUE),
                              gold_euro = mean(`EURO..AM.`, na.rm=TRUE))
knitr::kable(head(goldPricesYearly))
year gold_usd gold_gbp gold_euro
1968 38.82947 16.21790 NaN
1969 41.09988 17.19545 NaN
1970 35.96369 15.01211 NaN
1971 40.80680 16.67321 NaN
1972 58.17378 23.37925 NaN
1973 97.11735 39.54271 NaN

Czyszczenie danych na temat S&P Composite

spCompositeYearly <- spComposite
spCompositeYearly$year <- substr(spComposite$Year,1,4)
spCompositeYearly <- group_by(spCompositeYearly, year)
spCompositeYearly <- summarize(spCompositeYearly, 
                              spComposite = mean(`S.P.Composite`, na.rm=TRUE),
                              dividend = mean(Dividend, na.rm=TRUE),
                              earnings = mean(Earnings, na.rm=TRUE),
                              cpi = mean(CPI, na.rm=TRUE),
                              longInterestRate = mean(`Long.Interest.Rate`, na.rm=TRUE),
                              realPrice = mean(`Real.Price`, na.rm=TRUE),
                              realDividend = mean(`Real.Dividend`, na.rm=TRUE),
                              realEarnings = mean(`Real.Earnings`, na.rm=TRUE),
                              cyclicallyAdjustedPERattio = mean(`Cyclically.Adjusted.PE.Ratio`,na.rm=TRUE),
                              )
knitr::kable(head(spCompositeYearly))
year spComposite dividend earnings cpi longInterestRate realPrice realDividend realEarnings cyclicallyAdjustedPERattio
1871 4.691667 0.2600000 0.4000000 12.40064 5.338333 103.3978 5.727707 8.811857 NaN
1872 5.029167 0.2816667 0.4162500 12.92396 5.460833 106.2341 5.949608 8.793324 NaN
1873 4.801667 0.3162500 0.4462500 12.67816 5.529583 103.2745 6.823079 9.625514 NaN
1874 4.570000 0.3300000 0.4600000 11.94077 5.286667 104.5140 7.549957 10.524182 NaN
1875 4.447500 0.3137500 0.4058333 11.26684 4.850000 107.7608 7.601466 9.825189 NaN
1876 4.060833 0.3000000 0.3166667 10.50566 4.525833 105.5173 7.801827 8.229060 NaN

Podsumowanie danych

Podsumowanie WorldDevelopmentIndicators

knitr::kable(summary(cleanedWorldDevelopmentIndicatorsResult))
Country_Name Country_Code year Urban_population_growth_(annual_%) Urban_population_(%_of_total_population) Urban_population Transport_services_(%_of_commercial_service_exports) Transport_services_(%_of_commercial_service_imports) Trade_in_services_(%_of_GDP) Trade_(%_of_GDP) Total_natural_resources_rents_(%_of_GDP) Total_greenhouse_gas_emissions_(kt_of_CO2_equivalent) Taxes_less_subsidies_on_products_(current_US\() |Taxes_less_subsidies_on_products_(current_LCU) |Taxes_less_subsidies_on_products_(constant_LCU) |Survival_to_age_65,_female_(%_of_cohort) |Survival_to_age_65,_male_(%_of_cohort) |Services,_value_added_(%_of_GDP) |Service_imports_(BoP,_current_US\)) Service_exports_(BoP,_current_US\() |Secondary_education,_pupils |Rural_population_growth_(annual_%) |Rural_population_(%_of_total_population) |Rural_population |Renewable_energy_consumption_(%_of_total_final_energy_consumption) |Renewable_electricity_output_(%_of_total_electricity_output) |Pupil-teacher_ratio,_primary |Primary_income_payments_(BoP,_current_US\)) Primary_income_receipts_(BoP,_current_US\() |Primary_school_starting_age_(years) |Portfolio_investment,_net_(BoP,_current_US\)) Portfolio_equity,net_inflows(BoP,_current_US\() |Population,_total |Population,_male |Population,_male_(%_of_total_population) |Population,_female_(%_of_total_population) |Population,_female |Population_in_urban_agglomerations_of_more_than_1_million |Population_in_the_largest_city_(%_of_urban_population) |Population_in_largest_city |Population_growth_(annual_%) |Population_density_(people_per_sq._km_of_land_area) |Population_ages_65_and_above_(%_of_total_population) |Population_ages_15-64_(%_of_total_population) |Population_ages_0-14_(%_of_total_population) |Number_of_under-five_deaths |Nitrous_oxide_emissions_(thousand_metric_tons_of_CO2_equivalent) |Nitrous_oxide_emissions_in_energy_sector_(%_of_total) |Net_primary_income_(Net_income_from_abroad)_(current_US\)) Net_primary_income_(Net_income_from_abroad)_(current_LCU) Net_primary_income_(BoP,_current_US\() |Net_official_development_assistance_received_(current_US\)) Net_domestic_credit_(current_LCU) Natural_gas_rents_(%_of_GDP) Mortality_rate,infant(per_1,000_live_births) Methane_emissions_(kt_of_CO2_equivalent) Methane_emissions_in_energy_sector_(thousand_metric_tons_of_CO2_equivalent) Merchandise_exports_to_high-income_economies_(%_of_total_merchandise_exports) Manufacturing,value_added(%_of_GDP) Life_expectancy_at_birth,total(years) Land_area_(sq._km) Labor_force,_total Inflation,consumer_prices(annual_%) Individuals_using_the_Internet_(%_of_population) Imports_of_goods_and_services_(current_US\() |Imports_of_goods_and_services_(%_of_GDP) |Gross_savings_(%_of_GDP) |Gross_national_expenditure_(%_of_GDP) |Gross_national_expenditure_(current_US\)) Gross_savings_(current_US\() |Gross_domestic_savings_(%_of_GDP) |Gross_domestic_savings_(current_US\)) Goods_exports_(BoP,_current_US\() |Goods_imports_(BoP,_current_US\)) GDP_per_capita_(current_US\() |GDP_per_capita_growth_(annual_%) |GDP_growth_(annual_%) |GDP_(current_US\)) Fuel_exports_(%_of_merchandise_exports) Fuel_imports_(%_of_merchandise_imports) Food_exports_(%_of_merchandise_exports) Food_imports_(%_of_merchandise_imports) Exports_of_goods_and_services_(current_US\() |Exports_of_goods_and_services_(annual_%_growth) |Electricity_production_from_renewable_sources,_excluding_hydroelectric_(kWh) |Electricity_production_from_renewable_sources,_excluding_hydroelectric_(%_of_total) |Electricity_production_from_oil,_gas_and_coal_sources_(%_of_total) |Electricity_production_from_coal_sources_(%_of_total) |Electricity_production_from_hydroelectric_sources_(%_of_total) |Electricity_production_from_natural_gas_sources_(%_of_total) |Electricity_production_from_nuclear_sources_(%_of_total) |Consumer_price_index_(2010_=_100) |CO2_emissions_from_solid_fuel_consumption_(%_of_total) |CO2_emissions_from_solid_fuel_consumption_(kt) |CO2_emissions_from_transport_(%_of_total_fuel_combustion) |CO2_intensity_(kg_per_kg_of_oil_equivalent_energy_use) |CO2_emissions_from_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion) |CO2_emissions_from_other_sectors,_excluding_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion) |CO2_emissions_from_manufacturing_industries_and_construction_(%_of_total_fuel_combustion) |CO2_emissions_from_liquid_fuel_consumption_(kt) |CO2_emissions_from_liquid_fuel_consumption_(%_of_total) |CO2_emissions_from_gaseous_fuel_consumption_(kt) |CO2_emissions_from_gaseous_fuel_consumption_(%_of_total) |CO2_emissions_from_electricity_and_heat_production,_total_(%_of_total_fuel_combustion) |CO2_emissions_(metric_tons_per_capita) |CO2_emissions_(kt) |CO2_emissions_(kg_per_2010_US\)_of_GDP) Birth_rate,crude(per_1,000_people) Access_to_electricity_(%_of_population)
Length:10251 Length:10251 Min. :1970 Min. :-187.142 Min. : 2.845 Min. : 1267 Min. :-381.37 Min. : 0.292 Min. : 1.165 Min. : 0.021 Min. : 0.0000 Min. : 1 Min. :-14435512683 Min. :-125496297000000 Min. :-98323116751800 Min. : 6.464 Min. : 1.477 Min. :10.86 Min. : 912800 Min. : 0 Min. : 0 Min. :-235.7924 Min. : 0.00 Min. : 0 Min. : 0.000 Min. : 0.00 Min. : 5.226 Min. : -218731249 Min. : -50607238 Min. :4.000 Min. :-807954000000 Min. :-244072000000 Min. : 5740 Min. : 25278 Min. :44.37 Min. :23.29 Min. : 25864 Min. : 34329 Min. : 2.867 Min. : 18587 Min. :-10.9551 Min. : 0.136 Min. : 0.6856 Min. :45.45 Min. :11.05 Min. : 0 Min. : 0.0 Min. : 0.000 Min. :-99049385578 Min. :-481321056610000 Min. :-105172643986 Min. : -989940002 Min. : -54237641050700 Min. : 0.0000 Min. : 1.50 Min. : 0 Min. : 0 Min. : 0.0074 Min. : 0.000 Min. :18.91 Min. : 2 Min. : 31205 Min. : -18.109 Min. : 0.000 Min. : 0 Min. : 0.00 Min. :-236.27 Min. : 21.21 Min. : 18046707 Min. : -26010016894 Min. :-141.97 Min. : -7621878298 Min. : 199074 Min. : 5153121 Min. : 22.8 Min. :-64.9924 Min. :-64.047 Min. : 8824448 Min. : 0.000 Min. : 0.009 Min. : 0.000 Min. : 0.474 Min. : 693281 Min. : -96.4 Min. : 0 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : -4.324 Min. : -114 Min. : 0.00 Min. : 0.054 Min. : 0.000 Min. :-2.326 Min. : 0.00 Min. : -161 Min. : -6.089 Min. : -147 Min. : -0.7295 Min. : 0.00 Min. : 0.0000 Min. : 0 Min. :0.0000 Min. : 5.90 Min. : 0.534
Class :character Class :character 1st Qu.:1982 1st Qu.: 1.020 1st Qu.: 33.732 1st Qu.: 308785 1st Qu.: 12.50 1st Qu.:27.904 1st Qu.: 9.475 1st Qu.: 46.245 1st Qu.: 0.1967 1st Qu.: 7020 1st Qu.: 175995379 1st Qu.: 473000000 1st Qu.: 2155665300 1st Qu.:60.375 1st Qu.:51.602 1st Qu.:42.43 1st Qu.: 296306400 1st Qu.: 183033447 1st Qu.: 66416 1st Qu.: -0.4758 1st Qu.:25.78 1st Qu.: 233742 1st Qu.: 3.567 1st Qu.: 0.05 1st Qu.: 18.038 1st Qu.: 94167420 1st Qu.: 25349827 1st Qu.:6.000 1st Qu.: -109276346 1st Qu.: 0 1st Qu.: 724234 1st Qu.: 881327 1st Qu.:48.91 1st Qu.:49.65 1st Qu.: 835377 1st Qu.: 1078178 1st Qu.: 21.503 1st Qu.: 622567 1st Qu.: 0.5894 1st Qu.: 22.669 1st Qu.: 3.2186 1st Qu.:53.36 1st Qu.:23.35 1st Qu.: 655 1st Qu.: 499.6 1st Qu.: 2.617 1st Qu.: -1023369537 1st Qu.: -11588042500 1st Qu.: -1303373808 1st Qu.: 28555001 1st Qu.: 2524760134 1st Qu.: 0.0000 1st Qu.: 12.70 1st Qu.: 1650 1st Qu.: 140 1st Qu.: 56.0697 1st Qu.: 7.842 1st Qu.:59.16 1st Qu.: 13450 1st Qu.: 1022938 1st Qu.: 2.296 1st Qu.: 0.137 1st Qu.: 1228323588 1st Qu.: 25.09 1st Qu.: 14.76 1st Qu.: 97.69 1st Qu.: 4028319213 1st Qu.: 695514598 1st Qu.: 10.63 1st Qu.: 355315735 1st Qu.: 544473558 1st Qu.: 928350408 1st Qu.: 747.9 1st Qu.: -0.4303 1st Qu.: 1.169 1st Qu.: 2187406538 1st Qu.: 0.462 1st Qu.: 7.109 1st Qu.: 6.289 1st Qu.: 8.460 1st Qu.: 895671076 1st Qu.: -0.4 1st Qu.: 0 1st Qu.: 0.000 1st Qu.: 29.70 1st Qu.: 0.000 1st Qu.: 1.699 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 26.01 1st Qu.: 0.000 1st Qu.: 0 1st Qu.:17.75 1st Qu.: 1.548 1st Qu.: 4.651 1st Qu.: 0.302 1st Qu.:12.28 1st Qu.: 737 1st Qu.: 45.616 1st Qu.: 0 1st Qu.: 0.0000 1st Qu.:21.75 1st Qu.: 0.4834 1st Qu.: 1157 1st Qu.:0.2379 1st Qu.:15.00 1st Qu.: 71.958
Mode :character Mode :character Median :1995 Median : 2.323 Median : 53.806 Median : 2250864 Median : 23.42 Median :40.922 Median : 15.700 Median : 69.744 Median : 1.8564 Median : 30890 Median : 973745600 Median : 7987295600 Median : 28709021350 Median :77.149 Median :64.447 Median :51.69 Median : 1228100000 Median : 1112061730 Median : 395478 Median : 0.6670 Median :46.19 Median : 2090364 Median :18.187 Median : 13.67 Median : 25.339 Median : 589525169 Median : 198990841 Median :6.000 Median : 0 Median : 0 Median : 4999443 Median : 3041474 Median :49.65 Median :50.35 Median : 3061896 Median : 1999004 Median : 31.689 Median : 1300299 Median : 1.5532 Median : 71.453 Median : 4.6771 Median :60.53 Median :34.40 Median : 5266 Median : 3290.0 Median : 5.600 Median : -119592786 Median : -458840000 Median : -172276599 Median : 121305000 Median : 49615015000 Median : 0.0000 Median : 31.20 Median : 7480 Median : 900 Median : 72.8118 Median :12.693 Median :68.98 Median : 100250 Median : 3365515 Median : 5.295 Median : 6.828 Median : 5788616733 Median : 36.20 Median : 20.63 Median :102.96 Median : 18780239280 Median : 4452322244 Median : 20.34 Median : 3145461613 Median : 3356117901 Median : 4646974810 Median : 2604.8 Median : 1.9900 Median : 3.608 Median : 11109029840 Median : 3.126 Median :12.072 Median : 16.298 Median :12.711 Median : 4830010764 Median : 4.8 Median : 1000000 Median : 0.010 Median : 65.34 Median : 0.088 Median : 18.028 Median : 2.343 Median : 0.000 Median : 67.32 Median : 1.599 Median : 55 Median :26.70 Median : 2.306 Median : 9.244 Median : 2.345 Median :18.14 Median : 3689 Median : 72.856 Median : 0 Median : 0.2701 Median :34.29 Median : 2.0131 Median : 8140 Median :0.3707 Median :24.12 Median : 99.655
NA NA Mean :1995 Mean : 2.648 Mean : 54.198 Mean : 26466700 Mean : 26.88 Mean :41.536 Mean : 23.048 Mean : 81.157 Mean : 6.5016 Mean : 387149 Mean : 14914706105 Mean : 2785468080390 Mean : 2980869017620 Mean :71.795 Mean :62.219 Mean :51.33 Mean : 29028656406 Mean : 30179405859 Mean : 5481252 Mean : 0.4571 Mean :45.80 Mean : 30201302 Mean :30.318 Mean : 29.93 Mean : 28.076 Mean : 24343484422 Mean : 23535540188 Mean :6.155 Mean : -1824322116 Mean : 5287480742 Mean : 56402276 Mean : 31180725 Mean :49.91 Mean :50.09 Mean : 30761528 Mean : 9328817 Mean : 34.495 Mean : 2965607 Mean : 1.6761 Mean : 365.311 Mean : 6.8307 Mean :60.01 Mean :33.16 Mean : 92583 Mean : 28317.0 Mean : 8.913 Mean : -409419037 Mean : -908634940446 Mean : -467566683 Mean : 896634899 Mean : 42138130034400 Mean : 0.2629 Mean : 45.12 Mean : 73386 Mean : 24281 Mean : 67.8761 Mean :13.249 Mean :66.14 Mean : 1294572 Mean : 33196857 Mean : 27.050 Mean : 23.086 Mean : 122229047620 Mean : 43.31 Mean : 20.92 Mean :104.52 Mean : 496913394315 Mean : 72840190753 Mean : 18.97 Mean : 125588763362 Mean : 106020810582 Mean : 105299612634 Mean : 9998.5 Mean : 1.7475 Mean : 3.439 Mean : 426487447392 Mean : 16.013 Mean :13.614 Mean : 27.288 Mean :14.166 Mean : 124664660105 Mean : 145.8 Mean : 4688399069 Mean : 2.306 Mean : 59.27 Mean :16.145 Mean : 32.431 Mean : 17.886 Mean : 4.937 Mean : 75.38 Mean : 14.240 Mean : 106203 Mean :29.86 Mean : 2.329 Mean :10.819 Mean : 4.787 Mean :19.83 Mean : 99512 Mean : 68.350 Mean : 47892 Mean : 11.5945 Mean :34.68 Mean : 4.7991 Mean : 273387 Mean :0.5263 Mean :26.47 Mean : 81.593
NA NA 3rd Qu.:2008 3rd Qu.: 3.972 3rd Qu.: 74.216 3rd Qu.: 8198393 3rd Qu.: 37.37 3rd Qu.:54.216 3rd Qu.: 26.487 3rd Qu.:100.452 3rd Qu.: 8.0866 3rd Qu.: 102256 3rd Qu.: 6139212749 3rd Qu.: 101718788500 3rd Qu.: 246596317750 3rd Qu.:84.619 3rd Qu.:73.996 3rd Qu.:59.91 3rd Qu.: 6856026932 3rd Qu.: 6663954605 3rd Qu.: 1363938 3rd Qu.: 1.8501 3rd Qu.:66.27 3rd Qu.: 8248653 3rd Qu.:52.394 3rd Qu.: 56.99 3rd Qu.: 35.381 3rd Qu.: 4354306253 3rd Qu.: 2024107348 3rd Qu.:7.000 3rd Qu.: 28504340 3rd Qu.: 53856043 3rd Qu.: 16543472 3rd Qu.: 9844537 3rd Qu.:50.35 3rd Qu.:51.09 3rd Qu.: 9836732 3rd Qu.: 6761455 3rd Qu.: 43.737 3rd Qu.: 3030611 3rd Qu.: 2.6003 3rd Qu.: 169.919 3rd Qu.: 9.9226 3rd Qu.:66.06 3rd Qu.:43.29 3rd Qu.: 38411 3rd Qu.: 10980.0 3rd Qu.: 9.968 3rd Qu.: -25619 3rd Qu.: 0 3rd Qu.: -4510681 3rd Qu.: 394237495 3rd Qu.: 639905504977 3rd Qu.: 0.0717 3rd Qu.: 67.38 3rd Qu.: 26628 3rd Qu.: 5321 3rd Qu.: 84.5323 3rd Qu.:17.586 3rd Qu.:73.98 3rd Qu.: 472710 3rd Qu.: 10295119 3rd Qu.: 11.047 3rd Qu.: 41.435 3rd Qu.: 31047568760 3rd Qu.: 54.60 3rd Qu.: 26.72 3rd Qu.:110.22 3rd Qu.: 109114709491 3rd Qu.: 32636282215 3rd Qu.: 27.57 3rd Qu.: 28533420208 3rd Qu.: 25108000000 3rd Qu.: 25565975639 3rd Qu.: 10402.0 3rd Qu.: 4.3246 3rd Qu.: 6.009 3rd Qu.: 72060508273 3rd Qu.: 14.523 3rd Qu.:18.441 3rd Qu.: 43.011 3rd Qu.:18.043 3rd Qu.: 32174092994 3rd Qu.: 10.7 3rd Qu.: 452500000 3rd Qu.: 1.528 3rd Qu.: 92.00 3rd Qu.:26.107 3rd Qu.: 60.488 3rd Qu.: 24.422 3rd Qu.: 0.000 3rd Qu.: 100.00 3rd Qu.: 21.823 3rd Qu.: 6300 3rd Qu.:37.93 3rd Qu.: 2.861 3rd Qu.:15.143 3rd Qu.: 5.106 3rd Qu.:25.67 3rd Qu.: 27396 3rd Qu.: 94.731 3rd Qu.: 7811 3rd Qu.: 16.8106 3rd Qu.:46.99 3rd Qu.: 6.3299 3rd Qu.: 56975 3rd Qu.:0.6085 3rd Qu.:37.39 3rd Qu.:100.000
NA NA Max. :2020 Max. : 48.936 Max. :100.000 Max. :4352232429 Max. : 100.00 Max. :98.467 Max. :304.276 Max. :860.800 Max. :87.5074 Max. :45873850 Max. :774147989000 Max. : 651107600000000 Max. :450281900000000 Max. :96.093 Max. :92.978 Max. :96.20 Max. :5884869109190 Max. :6246125540220 Max. :601000000 Max. : 29.6283 Max. :97.16 Max. :3398794081 Max. :98.343 Max. :100.00 Max. :100.236 Max. :4858648262610 Max. :4790066687040 Max. :8.000 Max. : 282689352952 Max. :1257803920570 Max. :7752840547 Max. :3907216408 Max. :76.71 Max. :55.63 Max. :3842820324 Max. :409712858 Max. :100.000 Max. :37468302 Max. : 17.6334 Max. :21388.600 Max. :28.3973 Max. :86.40 Max. :51.57 Max. :12493789 Max. :2986520.0 Max. :192.227 Max. :292301000000 Max. : 105131000000000 Max. : 257794000000 Max. :167800328125 Max. :10211700000000000 Max. :22.4135 Max. :219.30 Max. :8174420 Max. :3187680 Max. :100.0000 Max. :50.037 Max. :85.42 Max. :129956634 Max. :3467973718 Max. :23773.132 Max. :100.000 Max. :24723587089500 Max. :427.58 Max. : 100.67 Max. :261.43 Max. :87149338258100 Max. :6256953481220 Max. : 88.39 Max. :23478971753200 Max. :19262553026400 Max. :19006596990600 Max. :190512.7 Max. :140.3670 Max. :149.973 Max. :87607773878100 Max. :359.256 Max. :94.057 Max. :354.553 Max. :62.416 Max. :25247985795000 Max. :844788.2 Max. :1644540000000 Max. :65.444 Max. :100.00 Max. :99.795 Max. :100.000 Max. :100.000 Max. :87.986 Max. :20422.89 Max. :216.648 Max. :15291329 Max. :96.97 Max. :103.158 Max. :48.431 Max. :86.957 Max. :81.25 Max. :10482498 Max. :258.524 Max. :7056781 Max. :207.3675 Max. :90.38 Max. :360.8532 Max. :34041046 Max. :5.3510 Max. :56.95 Max. :100.000
NA NA NA NA’s :85 NA’s :60 NA’s :83 NA’s :3971 NA’s :3819 NA’s :3969 NA’s :2895 NA’s :1978 NA’s :1542 NA’s :3786 NA’s :3742 NA’s :4515 NA’s :1101 NA’s :1101 NA’s :3731 NA’s :3728 NA’s :3726 NA’s :3845 NA’s :462 NA’s :60 NA’s :83 NA’s :4604 NA’s :5021 NA’s :4571 NA’s :3746 NA’s :3752 NA’s :527 NA’s :4102 NA’s :4541 NA’s :32 NA’s :950 NA’s :927 NA’s :927 NA’s :950 NA’s :4233 NA’s :2726 NA’s :2754 NA’s :35 NA’s :139 NA’s :927 NA’s :927 NA’s :927 NA’s :1948 NA’s :1340 NA’s :3186 NA’s :2661 NA’s :2622 NA’s :3771 NA’s :3713 NA’s :3289 NA’s :2618 NA’s :1677 NA’s :1360 NA’s :1098 NA’s :2198 NA’s :3768 NA’s :1015 NA’s :116 NA’s :4798 NA’s :3295 NA’s :4639 NA’s :2874 NA’s :2894 NA’s :4765 NA’s :3374 NA’s :3309 NA’s :4782 NA’s :3230 NA’s :3264 NA’s :3730 NA’s :3727 NA’s :1835 NA’s :2077 NA’s :2074 NA’s :1832 NA’s :4516 NA’s :4134 NA’s :4154 NA’s :4126 NA’s :2875 NA’s :4224 NA’s :4668 NA’s :4671 NA’s :4671 NA’s :4671 NA’s :4671 NA’s :4671 NA’s :4772 NA’s :3236 NA’s :2433 NA’s :2062 NA’s :4775 NA’s :4795 NA’s :4775 NA’s :4775 NA’s :4775 NA’s :2062 NA’s :2433 NA’s :2062 NA’s :2433 NA’s :4775 NA’s :1963 NA’s :1960 NA’s :3046 NA’s :808 NA’s :5063

Podsumowanie GoldPrices

knitr::kable(summary(goldPricesYearly))
year gold_usd gold_gbp gold_euro
Length:54 Min. : 35.96 Min. : 15.01 Min. : 261.6
Class :character 1st Qu.: 283.00 1st Qu.: 179.26 1st Qu.: 344.1
Mode :character Median : 382.50 Median : 241.59 Median : 926.3
NA Mean : 580.96 Mean : 374.95 Mean : 805.1
NA 3rd Qu.: 828.39 3rd Qu.: 441.39 3rd Qu.:1121.5
NA Max. :1801.00 Max. :1379.37 Max. :1549.5
NA NA NA NA’s :31

Podsumowanie SpComposite

knitr::kable(summary(spCompositeYearly))
year spComposite dividend earnings cpi longInterestRate realPrice realDividend realEarnings cyclicallyAdjustedPERattio
Length:151 Min. : 3.136 Min. : 0.1800 Min. : 0.2058 Min. : 6.462 Min. : 0.8942 Min. : 84.79 Min. : 5.728 Min. : 7.871 Min. : 5.311
Class :character 1st Qu.: 7.894 1st Qu.: 0.4217 1st Qu.: 0.5704 1st Qu.: 10.212 1st Qu.: 3.2185 1st Qu.: 186.28 1st Qu.: 9.365 1st Qu.: 14.529 1st Qu.:12.003
Mode :character Median : 17.081 Median : 0.8933 Median : 1.4217 Median : 20.042 Median : 3.8196 Median : 281.08 Median :14.331 Median : 22.892 Median :16.457
NA Mean : 332.147 Mean : 6.9012 Mean : 15.7599 Mean : 62.619 Mean : 4.5006 Mean : 625.91 Mean :17.637 Mean : 35.241 Mean :17.237
NA 3rd Qu.: 160.446 3rd Qu.: 7.1400 3rd Qu.: 14.9679 3rd Qu.:101.742 3rd Qu.: 5.0492 3rd Qu.: 711.64 3rd Qu.:22.489 3rd Qu.: 43.987 3rd Qu.:20.774
NA Max. :4114.705 Max. :59.0941 Max. :134.9175 Max. :267.817 Max. :13.9108 Max. :4172.50 Max. :62.339 Max. :144.072 Max. :42.068
NA NA NA NA NA NA NA NA NA NA’s :10

Podsumowanie CurrencyExchangeRates

knitr::kable(summary(currencyExchangeRates))
Date Algerian.Dinar Australian.Dollar Bahrain.Dinar Bolivar.Fuerte Botswana.Pula Brazilian.Real Brunei.Dollar Canadian.Dollar Chilean.Peso Chinese.Yuan Colombian.Peso Czech.Koruna Danish.Krone Euro Hungarian.Forint Icelandic.Krona Indian.Rupee Indonesian.Rupiah Iranian.Rial Israeli.New.Sheqel Japanese.Yen Kazakhstani.Tenge Korean.Won Kuwaiti.Dinar Libyan.Dinar Malaysian.Ringgit Mauritian.Rupee Mexican.Peso Nepalese.Rupee New.Zealand.Dollar Norwegian.Krone Nuevo.Sol Pakistani.Rupee Peso.Uruguayo Philippine.Peso Polish.Zloty Qatar.Riyal Rial.Omani Russian.Ruble Saudi.Arabian.Riyal Singapore.Dollar South.African.Rand Sri.Lanka.Rupee Swedish.Krona Swiss.Franc Thai.Baht Trinidad.And.Tobago.Dollar Tunisian.Dinar U.A.E..Dirham U.K..Pound.Sterling U.S..Dollar
Length:5978 Min. : 71.29 Min. :0.4833 Min. :0.376 Min. : 2.14 Min. :0.0855 Min. :0.832 Min. :1.000 Min. :0.917 Min. :377.5 Min. :6.093 Min. : 833.2 Min. :14.45 Min. :4.665 Min. :0.8252 Min. :144.1 Min. : 54.72 Min. :31.37 Min. : 2201 Min. : 1699 Min. :3.230 Min. : 75.86 Min. :117.2 Min. : 756 Min. :0.2646 Min. :0.525 Min. :2.436 Min. :25.15 Min. : 5.915 Min. : 49.88 Min. :0.3927 Min. :4.959 Min. :2.539 Min. : 30.88 Min. : 9.32 Min. :24.55 Min. :2.022 Min. :3.64 Min. :0.3845 Min. :23.13 Min. :3.745 Min. :1.201 Min. : 3.530 Min. : 49.57 Min. : 5.843 Min. :0.7253 Min. :24.44 Min. :5.839 Min. :1.342 Min. :3.671 Min. :1.213 Min. :1
Class :character 1st Qu.: 77.50 1st Qu.:0.6654 1st Qu.:0.376 1st Qu.: 2.59 1st Qu.:0.1197 1st Qu.:1.709 1st Qu.:1.348 1st Qu.:1.086 1st Qu.:503.5 1st Qu.:6.495 1st Qu.:1786.0 1st Qu.:19.35 1st Qu.:5.612 1st Qu.:1.0889 1st Qu.:202.7 1st Qu.: 70.28 1st Qu.:42.82 1st Qu.: 8855 1st Qu.: 1755 1st Qu.:3.676 1st Qu.:100.70 1st Qu.:145.4 1st Qu.:1013 1st Qu.:0.2854 1st Qu.:0.662 1st Qu.:3.188 1st Qu.:29.12 1st Qu.:10.953 1st Qu.: 68.33 1st Qu.:0.5813 1st Qu.:6.104 1st Qu.:2.755 1st Qu.: 51.79 1st Qu.:20.07 1st Qu.:43.18 1st Qu.:3.033 1st Qu.:3.64 1st Qu.:0.3845 1st Qu.:28.27 1st Qu.:3.745 1st Qu.:1.361 1st Qu.: 6.213 1st Qu.: 77.54 1st Qu.: 6.838 1st Qu.:0.9777 1st Qu.:31.50 1st Qu.:6.260 1st Qu.:1.566 1st Qu.:3.672 1st Qu.:1.519 1st Qu.:1
Mode :character Median : 81.28 Median :0.7595 Median :0.376 Median : 6.28 Median :0.1528 Median :2.048 Median :1.468 Median :1.297 Median :538.6 Median :6.989 Median :2017.6 Median :21.88 Median :6.051 Median :1.2295 Median :224.3 Median : 83.48 Median :45.92 Median : 9260 Median : 8992 Median :3.882 Median :109.39 Median :150.3 Median :1122 Median :0.2947 Median :1.932 Median :3.676 Median :30.67 Median :12.680 Median : 74.04 Median :0.6844 Median :6.709 Median :2.819 Median : 60.75 Median :22.94 Median :44.40 Median :3.290 Median :3.64 Median :0.3845 Median :30.54 Median :3.750 Median :1.444 Median : 7.480 Median :103.99 Median : 7.618 Median :1.1878 Median :34.65 Median :6.282 Median :1.723 Median :3.672 Median :1.599 Median :1
NA Mean : 90.59 Mean :0.7683 Mean :0.376 Mean : 835.09 Mean :0.1965 Mean :2.161 Mean :1.508 Mean :1.268 Mean :561.8 Mean :7.316 Mean :2073.1 Mean :22.95 Mean :6.281 Mean :1.2076 Mean :231.1 Mean : 92.46 Mean :48.02 Mean : 9144 Mean :10718 Mean :4.003 Mean :107.97 Mean :185.6 Mean :1100 Mean :0.2936 Mean :1.510 Mean :3.508 Mean :31.03 Mean :13.116 Mean : 77.37 Mean :0.6606 Mean :6.965 Mean :2.960 Mean : 70.24 Mean :24.11 Mean :45.01 Mean :3.365 Mean :3.64 Mean :0.3845 Mean :36.91 Mean :3.749 Mean :1.503 Mean : 8.113 Mean :102.19 Mean : 7.741 Mean :1.2090 Mean :35.14 Mean :6.310 Mean :1.850 Mean :3.672 Mean :1.615 Mean :1
NA 3rd Qu.:108.88 3rd Qu.:0.8689 3rd Qu.:0.376 3rd Qu.: 6.28 3rd Qu.:0.1844 3rd Qu.:2.794 3rd Qu.:1.698 3rd Qu.:1.409 3rd Qu.:619.8 3rd Qu.:8.277 3rd Qu.:2482.9 3rd Qu.:24.94 3rd Qu.:6.805 3rd Qu.:1.3338 3rd Qu.:267.6 3rd Qu.:117.15 3rd Qu.:52.33 3rd Qu.:11380 3rd Qu.:11180 3rd Qu.:4.370 3rd Qu.:118.38 3rd Qu.:185.7 3rd Qu.:1186 3rd Qu.:0.3027 3rd Qu.:1.932 3rd Qu.:3.800 3rd Qu.:32.89 3rd Qu.:13.668 3rd Qu.: 86.80 3rd Qu.:0.7364 3rd Qu.:7.806 3rd Qu.:3.243 3rd Qu.: 94.29 3rd Qu.:28.44 3rd Qu.:47.10 3rd Qu.:3.822 3rd Qu.:3.64 3rd Qu.:0.3845 3rd Qu.:36.20 3rd Qu.:3.750 3rd Qu.:1.687 3rd Qu.: 9.995 3rd Qu.:126.29 3rd Qu.: 8.384 3rd Qu.:1.3903 3rd Qu.:39.45 3rd Qu.:6.382 3rd Qu.:2.157 3rd Qu.:3.672 3rd Qu.:1.676 3rd Qu.:1
NA Max. :115.58 Max. :1.1055 Max. :0.376 Max. :68827.50 Max. :4.8414 Max. :4.195 Max. :1.851 Max. :1.613 Max. :758.2 Max. :8.746 Max. :3434.9 Max. :40.29 Max. :9.006 Max. :1.5990 Max. :318.7 Max. :147.98 Max. :68.78 Max. :14850 Max. :42000 Max. :4.994 Max. :147.00 Max. :383.9 Max. :1965 Max. :0.3089 Max. :1.932 Max. :4.725 Max. :36.50 Max. :21.908 Max. :109.98 Max. :0.8822 Max. :9.606 Max. :3.522 Max. :115.70 Max. :32.53 Max. :52.35 Max. :4.500 Max. :3.64 Max. :0.3845 Max. :83.59 Max. :3.750 Max. :1.851 Max. :16.771 Max. :157.65 Max. :10.995 Max. :1.8228 Max. :56.06 Max. :6.789 Max. :2.509 Max. :3.675 Max. :2.102 Max. :1
NA NA’s :4112 NA’s :263 NA’s :69 NA’s :3664 NA’s :1275 NA’s :539 NA’s :1246 NA’s :356 NA’s :1220 NA’s :1316 NA’s :582 NA’s :1850 NA’s :251 NA’s :1070 NA’s :1415 NA’s :354 NA’s :429 NA’s :1492 NA’s :1312 NA’s :1939 NA’s :316 NA’s :3051 NA’s :601 NA’s :1054 NA’s :123 NA’s :301 NA’s :2460 NA’s :2266 NA’s :479 NA’s :310 NA’s :291 NA’s :4297 NA’s :488 NA’s :4287 NA’s :4198 NA’s :1765 NA’s :47 NA’s :56 NA’s :2435 NA’s :46 NA’s :259 NA’s :535 NA’s :509 NA’s :349 NA’s :239 NA’s :565 NA’s :657 NA’s :4258 NA’s :71 NA’s :122 NA

Połącznie danych dotyczących rozwoju świata, cen zlota i stanu gospodarki

worldIndicatorsForWholeWorld <- worldIndicatorsComplementedDf %>%
  filter(Country_Name == 'World')

goldPricesAndWorldIndicators <- merge(x=worldIndicatorsForWholeWorld[-c(1,2)], y=goldPricesYearly[c('year','gold_usd')], by='year')
goldPricesAndSpcomposite = merge(y=goldPricesYearly[c('year','gold_usd')], x=spCompositeYearly, by='year')

Znalezienie atrybutów dotyczących rozwoju świata znacząco wpływających na ceny złota

control <- trainControl(method="repeatedcv", number=10, repeats=5)
rfGrid <- expand.grid(mtry = 10:30)
model <- train(
  gold_usd~., 
  data=subset(goldPricesAndWorldIndicators, select = -year), 
  method="rf", 
  na.action = na.pass,
  tuneGrid = rfGrid,
  ntree = 30,
  trControl=control)

importance <- arrange(varImp(model)$importance, desc(Overall)) 
mostImoortantIndicators_Gold <- head(importance, n=15)
knitr::kable(mostImoortantIndicators_Gold)
Overall
Population_ages_0-14_(%_of_total_population) 100.00000
Rural_population_(%_of_total_population) 58.21818
GDP_per_capita_(current_US$) 31.23685
Goods_imports_(BoP,_current_US$) 27.25942
Imports_of_goods_and_services_(current_US$) 25.80833
Population,_total 21.02249
Population,_male 18.28670
Population,_male_(%_of_total_population) 18.12738
GDP_(current_US$) 16.98931
Population_growth_(annual_%) 16.45479
Population_in_the_largest_city_(%_of_urban_population) 14.80549
Labor_force,_total 14.59779
CO2_emissions_from_other_sectors 14.38574
Pupil-teacher_ratio,_primary 13.56321
Trade_in_services_(%_of_GDP) 13.53011

Macierz korelacji uzyskanych danych

names <- c(rownames(mostImoortantIndicators_Gold), 'year')
names <- gsub("`", "", all_of(names))
dataForCorrelation <- merge(worldIndicatorsForWholeWorld[names], goldPricesAndSpcomposite, by='year')
res <- cor(dataForCorrelation)

heatmaply_cor(
  res,
  xlab = "Features", 
  ylab = "Features",
  k_col = 2, 
  k_row = 2
)

Macierz korelacji wyłącznie rozwoju krajów

res <- cor(worldIndicatorsComplementedDf[names])

heatmaply_cor(
  res,
  xlab = "Features", 
  ylab = "Features",
  k_col = 2, 
  k_row = 2
)

Wykresy dotyczące wpływu czynników rozwoju świata na ceny złota

g <- ggplot(
  goldPricesYearly, 
  aes(x=year, 
      y=gold_usd,
      group=1)
) +
scale_x_discrete(limits=goldPricesYearly$year,breaks=goldPricesYearly$year[seq(1,length(goldPricesYearly$year),by=5)]) +
geom_line()
ggplotly(g)
g <- ggplot(
  goldPricesAndWorldIndicators, 
  aes(x=`CO2_emissions_residential_buildings_and_services`, 
      y=gold_usd,
      group=1)
) +
geom_line()
ggplotly(g)
g <- ggplot(
  goldPricesAndWorldIndicators, 
  aes(x=`Birth_rate,_crude_(per_1,000_people)`, 
      y=gold_usd,
      group=1)
) +
geom_line()
ggplotly(g)
g <- ggplot(
  goldPricesAndWorldIndicators, 
  aes(x=Rural_population, 
      y=gold_usd,
      group=1)
) +
geom_line()
ggplotly(g)
g <- ggplot(
  goldPricesAndWorldIndicators, 
  aes(x=`GDP_(current_US$)`, 
      y=gold_usd,
      group=1)
) +
geom_line()
ggplotly(g)

Wykres przedstawiający wzrost S&P Composite w czasie

g <- ggplot(
  spCompositeYearly, 
  aes(x=year, 
      y=spComposite,
      group=1)
) +
geom_line()
ggplotly(g)

Wskaźnik PKB w poszczególnych krajach.

cleanedWorldDevelopmentIndicatorsResultWithoutWorld <- cleanedWorldDevelopmentIndicatorsResult %>%
    filter(Country_Name != 'World')
max_gdp_country = max(cleanedWorldDevelopmentIndicatorsResultWithoutWorld$`GDP_(current_US$)`, na.rm = TRUE)
scaleFactor <- max_gdp_country / max(cleanedWorldDevelopmentIndicatorsResultWithoutWorld$`GDP_per_capita_(current_US$)`, na.rm = TRUE)

g <- ggplot(
  cleanedWorldDevelopmentIndicatorsResultWithoutWorld,
  aes(x=year)
) +
geom_smooth(aes(y=`GDP_(current_US$)`),  method="loess", col="blue") +
geom_smooth(aes(y=`GDP_per_capita_(current_US$)` * scaleFactor),  method="loess", col="red") +
scale_y_continuous(name="GDP(current US$)", sec.axis=sec_axis(~./scaleFactor, name="GDP per capita(current US$)")) +
theme(
    axis.title.y.left=element_text(color="blue"),
    axis.text.y.left=element_text(color="blue"),
    axis.title.y.right=element_text(color="red"),
    axis.text.y.right=element_text(color="red")
  ) +
facet_wrap(vars(Country_Name), ncol = 4)
ggplotly(g)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1832 rows containing non-finite values (stat_smooth).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1835 rows containing non-finite values (stat_smooth).

Regresja przewidująca cenę złota na podstawie wielu danych

idx <- 
    createDataPartition(
        dataForCorrelation$gold_usd,
        p = .75,
        list = FALSE)

training <- dataForCorrelation[idx,]
testing <- dataForCorrelation[-idx,]

control <- trainControl(method="repeatedcv", number=2, repeats=5)
rfGrid <- expand.grid(mtry = 10:30)
modelRandomForest <- train(
  gold_usd~., 
  data=training, 
  method="rf", 
  na.action = na.pass,
  tuneGrid = rfGrid,
  ntree = 10,
  trControl=control)
modelBGLm <- train(
  gold_usd~., 
  data=training, 
  method="bayesglm", 
  na.action = na.pass,
  trControl=control)
lGrid <- expand.grid(intercept = 1:20)
modelLogicBag <- train(
  gold_usd~., 
  data=training, 
  method="lm", 
  na.action = na.pass,
  tuneGrid = lGrid,
  trControl=control)
rfPred <- predict(modelRandomForest , testing)
knitr::kable(postResample(pred = rfPred, obs = testing$gold_usd))
x
RMSE 96.7319983
Rsquared 0.9859129
MAE 70.9918455 
bPred <- predict(modelBGLm , testing)
knitr::kable(postResample(pred = bPred, obs = testing$gold_usd))
x
RMSE 67.7489265
Rsquared 0.9834946
MAE 54.7454446 
lPred <- predict(modelLogicBag , testing)
knitr::kable(postResample(pred = lPred, obs = testing$gold_usd))
x
RMSE 138.9622071
Rsquared 0.8886545
MAE 121.3133067

Wazność atrybutów dla modelu RandomForest

importance <- arrange(varImp(modelRandomForest)$importance, desc(Overall))
knitr::kable(importance)
Overall
earnings 100.0000000
Population_growth_(annual_%) 74.5219258
Labor_force,_total 72.9680997
cpi 46.7204214
GDP_per_capita_(current_US$) 44.1429126
Pupil-teacher_ratio,_primary 43.8581610
Trade_in_services_(%_of_GDP) 39.0344402
longInterestRate 6.1684898
Rural_population_(%_of_total_population) 5.9774875
year 4.9353340
spComposite 4.5874077
Population,_total 4.1002218
Population,_male_(%_of_total_population) 3.5388833
cyclicallyAdjustedPERattio 1.5940145
dividend 1.2406663
realDividend 1.0025680
Imports_of_goods_and_services_(current_US$) 0.8432858
realEarnings 0.6355736
realPrice 0.5670825
Population_in_the_largest_city_(%_of_urban_population) 0.4499756
Population_ages_0-14_(%_of_total_population) 0.3900090
Goods_imports_(BoP,_current_US$) 0.1816233
CO2_emissions_from_other_sectors 0.1058365
Population,_male 0.0473619
GDP_(current_US$) 0.0000000

Regresja przewidująca cenę złota używając wyłącznie danych dotyczących rozwoju świata

idx <- 
    createDataPartition(
        goldPricesAndWorldIndicators$gold_usd,
        p = .75,
        list = FALSE)

training <- goldPricesAndWorldIndicators[idx,]
testing <- goldPricesAndWorldIndicators[-idx,]

control <- trainControl(method="repeatedcv", number=2, repeats=5)
rfGrid <- expand.grid(mtry = 10:30)
modelRandomForest_WorldIndicators <- train(
  gold_usd~., 
  data=training, 
  method="rf", 
  na.action = na.pass,
  tuneGrid = rfGrid,
  ntree = 15,
  trControl=control)
indPred <- predict(modelRandomForest_WorldIndicators , testing)
knitr::kable(postResample(pred = indPred, obs = testing$gold_usd))
x
RMSE 104.7064181
Rsquared 0.9638511
MAE 66.6148946

Ważność atrynbutów

importance <- arrange(varImp(modelRandomForest_WorldIndicators)$importance, desc(Overall))
knitr::kable(head(importance, 20))
Overall
Pupil-teacher_ratio,_primary 100.000000
Merchandise_exports_to_high-income_economies_(%_of_total_merchandise_exports) 83.712966
Population,_female_(%_of_total_population) 82.555846
Population_in_the_largest_city_(%_of_urban_population) 77.119595
Population_growth_(annual_%) 53.393064
CO2_emissions_residential_buildings_and_services 45.236096
Population_ages_0-14_(%_of_total_population) 33.497220
Population_ages_65_and_above_(%_of_total_population) 29.543570
Survival_to_age_65,_female_(%_of_cohort) 26.947180
Life_expectancy_at_birth,_total_(years) 22.098374
Urban_population 20.745456
Goods_exports_(BoP,_current_US$) 7.117197
Population,_female 5.035787
Access_to_electricity_(%_of_population) 4.588349
Population,_male 4.474118
Rural_population_(%_of_total_population) 3.993420
Net_official_development_assistance_received_(current_US$) 3.992536
CO2_intensity_(kg_per_kg_of_oil_equivalent_energy_use) 3.966994
Food_imports_(%_of_merchandise_imports) 3.630329
Nitrous_oxide_emissions_(thousand_metric_tons_of_CO2_equivalent) 3.336564